Evidence map›Paper›PMID 36339085›Full record

ArticleEngineering analysis with boundary elements2023

A mathematical model of coronavirus transmission by using the heuristic computing neural networks.

Zulqurnain Sabir, Adi Asmara, Sanaullah Dehraj, Muhammad Asif Zahoor Raja, Gilder Cieza Altamirano, Soheil Salahshour, R Sadat, Mohamed R Ali

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Article in Engineering analysis with boundary elements, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zulqurnain SabirDepartment of Mathematics and Statistics, Hazara University, Mansehra, Pakistan.
Adi AsmaraFaculty of Teacher Training and Education, Mathematics Study Program, Universitas Muhammadiyah Bengkulu, Bengkulu, Indonesia.
Sanaullah DehrajDepartment of Mathematics and Statistics, Quaid-e-Awam University of Engineering, Science and Technology, 67480 Nawabshah, Pakistan.
Muhammad Asif Zahoor RajaFuture Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan.
Gilder Cieza AltamiranoUniversidad Nacional Autónoma de Chota, Chota, Perú.
Soheil SalahshourFaculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey.
R SadatDepartment of Mathematics, Zagazig Faculty of Engineering, Zagazig University, Egypt.
Mohamed R AliCenter of Research, Faculty of Engineering and Technology, Future University in Egypt New Cairo, 11835, Egypt.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, the nonlinear mathematical model of COVID-19 is investigated by stochastic solver using the scaled conjugate gradient neural networks (SCGNNs). The nonlinear mathematical model of COVID-19 is represented by coupled system of ordinary differential equations and is studied for three different cases of initial conditions with suitable parametric values. This model is studied subject to seven class of human population

Indexed as

COVID-19NonlinearNumerical resultsQuarantineRunge-Kutta schemeScaled conjugate gradientSCGNNs

Identifiers

PMID36339085
PMCPMC9618448

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.